Quantum computers with their interacting qubits as basic units appear very promising for optimization problems with binary
variables such as those present in spin systems. These technologies are being developed along two main tracks:
quantum annealers [1]} and gate-based systems [2]}.
In quantum annealing (QA) [3]}, [4]}, [5]}, the idea is to encode the solution to a given
optimization problem in the ground state of a Hamiltonian and efficiently locate
the energy minimum by exploiting quantum fluctuations and tunneling, in analogy with the
role played by thermal fluctuations in classical simulated annealing (SA) [6]}.
Mapping difficult binary optimization problems onto Ising spin glass systems goes back to the eighties in the context of
neural networks [7]}, [8]}. For a recent review, see Ref. [9]}.
In the context of QA, this approach is called quadratic unconstrained binary optimization (QUBO).
